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Predicting Student Dropout: A Replication Study Based on Neural Networks

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  • Jascha Buchhorn
  • Berthold U. Wigger

Abstract

Using neural networks, the present study replicates previous results on the prediction of student dropout obtained with decision trees and logistic regressions. For this purpose, multilayer perceptrons are trained on the same data as in the initial study. It is shown that neural networks lead to a significant improvement in the prediction of students at risk. Already after the first semester, potential dropouts can be identified with a probability of 95 percent.

Suggested Citation

  • Jascha Buchhorn & Berthold U. Wigger, 2021. "Predicting Student Dropout: A Replication Study Based on Neural Networks," CESifo Working Paper Series 9300, CESifo.
    • Handle: RePEc:ces:ceswps:_9300
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    Keywords

    neural networks; student dropout; replication study;
    All these keywords.

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